CN111081106A - Job pushing method, system, equipment and storage medium - Google Patents
Job pushing method, system, equipment and storage medium Download PDFInfo
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- CN111081106A CN111081106A CN201911298867.9A CN201911298867A CN111081106A CN 111081106 A CN111081106 A CN 111081106A CN 201911298867 A CN201911298867 A CN 201911298867A CN 111081106 A CN111081106 A CN 111081106A
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- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/06—Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer-type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers
- G09B7/07—Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer-type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers providing for individual presentation of questions to a plurality of student stations
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Abstract
The embodiment of the application provides a method, a system, equipment and a storage medium for pushing operation, wherein the method comprises the following steps: acquiring historical learning data, and judging whether the historical learning data meets a first preset condition; if the first preset condition is met, determining the range of the knowledge points involved in the operation according to the historical learning data; determining knowledge points needing to be learned through operation according to the knowledge point range, and pushing operation matched with the knowledge points; and acquiring homework answer data of students, judging whether the homework answer data meets a second preset condition, and stopping homework pushing when the homework answer data meets the second preset condition. The embodiment of the application pushes homework with the student according to the historical learning data of the student, and updates the mastery degree of the student on the knowledge points in real time so as to enable the student to master weak knowledge points with the least homework.
Description
Technical Field
The present application relates to the field of electrical technologies, and in particular, to a method, a system, a device, and a storage medium for job pushing.
Background
At present, homework is difficult to be arranged according to the learning condition of each student in an individualized way, so that mastered students can complete homework mechanically and repeatedly, while some students neglect knowledge which is not mastered, and the efficacy of homework is greatly reduced. The existing system and method can acquire the learning condition of the student and push the homework according to the learning data or homework score of the student, but can not update the mastering probability of the student on the knowledge points in real time to judge whether the student masters the learned knowledge points so as to avoid repeated exercise and energy consumption of the student.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method, a system, a device and a storage medium for pushing homework, so as to push appropriate homework according to the knowledge mastering degree of students, and update the mastering probability of the students on learned knowledge points in real time by using the homework answer data of the students.
In a first aspect, an embodiment of the present application provides a job pushing method, including acquiring historical learning data, and determining whether the historical learning data meets a first preset condition; if the first preset condition is met, determining the range of the knowledge points involved in the operation according to the historical learning data; determining knowledge points needing to be learned through operation according to the knowledge point range, and pushing operation matched with the knowledge points; and acquiring homework answer data of students, judging whether the homework answer data meets a second preset condition, and stopping homework pushing when the homework answer data meets the second preset condition.
In the implementation process, firstly, historical learning data of a student are obtained, whether the historical learning data meet a first preset condition or not is judged, if yes, a knowledge point range of homework is determined according to the obtained historical learning data, then, knowledge points needing to be learned through homework by the student in the knowledge point range are determined, homework matched with the knowledge points is pushed to the student, the current learning condition of the student on each knowledge point can be clearly judged through the historical learning data of the student, and therefore a knowledge point learning range which meets the current learning condition of the student can be obtained. To push homework suitable for the student to practice at present to the student; furthermore, in the student answering process, homework answering data of the students are obtained in real time, whether second preset conditions are met or not is judged, whether the homework pushing to the students is stopped or not is judged, and therefore repeated practice of the students on mastered knowledge points is avoided.
Further, the judging whether the historical learning data meets the first preset condition includes: judging whether the historical learning data meet the triggering condition; the trigger conditions include: weak knowledge points are displayed on classroom learning data of students in first preset time, forgetting knowledge points are displayed on classroom learning data of the students in the first preset time, and one or more kinds of knowledge points to be strengthened of the students are displayed on the classroom learning data of the students in the first preset time.
In the implementation process, whether the student is weak, forgets or has knowledge points to be strengthened or not within a first preset time is judged according to the acquired historical learning data of the student, and if one or more of the knowledge points exist, the first preset condition is judged to be met, so that the student can push homework as required.
Further, the judging whether the historical learning data meets a first preset condition further comprises: judging whether the current time point belongs to preset trigger time or not; the preset trigger time comprises the following steps: the teacher quits the time of the online classroom, the manual trigger time of the teacher and the automatic trigger time of each week.
In the implementation process, the current time point belongs to the time when the teacher quits the online classroom, and the teacher judges that the first preset condition is met when the teacher triggers manually or automatically at regular time every week, so that the teacher can freely push homework to students according to the preset trigger time.
Further, if the first preset condition is met, determining the knowledge point range related to the operation according to the historical learning data includes: determining the learning range of the student within a second preset time according to the historical learning data; analyzing the mastering probability of the knowledge points in the learning range based on a depth knowledge tracking model; according to the grasping probability, predicting the probability of the student grasping each knowledge point through the homework through a multi-level mixed model; and determining the range of the knowledge points involved in the operation according to the predicted knowledge point mastering probability.
In the implementation process, the learning range of the student within the second preset time is determined according to the acquired historical learning data of the student, and then the knowledge point mastering probability of the student within the learning range is analyzed by using a deep knowledge tracking model, so that the mastering condition of the student on each knowledge point can be accurately obtained; furthermore, a multi-level mixed model is adopted to predict the probability of the students mastering each knowledge point through the homework, and the knowledge point range related to the homework is determined according to the predicted knowledge point mastering probability, so that the range of the knowledge points suitable for the students to learn through the homework can be obtained according to the knowledge point mastering probability of the students.
Further, the determining the knowledge points needing to be learned through the operation according to the knowledge point range and pushing the operation matched with the knowledge points comprises: determining knowledge points needing to be learned in operation in the knowledge point range based on a knowledge graph according to the historical learning data; and pushing the operation matched with the knowledge point.
In the implementation process, according to historical learning data of students, the knowledge map is used for determining the next knowledge point which needs to be learned by the students in the homework within the knowledge point range, and the homework matched with the determined knowledge point is pushed to the students, so that the students can be pushed with proper homework according to the knowledge mastering degree of the students.
Further, the obtaining student homework answer data and judging whether the student homework answer data meets a second preset condition comprise: extracting homework answering data of students; judging whether the number of the knowledge points mastered by the students reaches a first preset upper limit or not according to the homework answer data, and judging that the second preset condition is met when the students master all the knowledge points in the knowledge point range; or judging whether the number of the operation questions reaches a second preset upper limit or not according to the operation answer data, and judging that the second preset condition is met when the number of the operation questions reaches the second preset upper limit.
In the implementation process, in the process of making the questions by the students, homework answer data of the questions answered by the students are extracted in real time, whether the number of the knowledge points mastered by the students reaches a first preset upper limit or not is judged according to the homework answer data, whether the number of the homework questions made by the students reaches a second preset upper limit or not is judged according to the homework answer data, when the number of the knowledge points mastered by the students reaches the first preset upper limit or the number of the homework questions made by the students reaches the second preset upper limit, pushing is stopped, and accordingly, the students can be timely stopped from pushing the homework matched with the mastered knowledge points.
Further, the judging whether the number of the knowledge points mastered by the student reaches a first preset upper limit according to the homework answer data includes: updating the knowledge point mastering degree of the student according to the homework answering data; and judging whether the student grasps the knowledge points according to the grasping degree of the knowledge points.
In the implementation process, the mastery degree of the students on the learned knowledge points is acquired in real time according to the homework answer data of the students, so that the learning condition of the students on the knowledge points can be tracked in real time; furthermore, whether the student grasps the learned knowledge points is judged according to the grasping degree of the knowledge points, so that the pushing of homework matched with the knowledge points can be stopped in time, and repeated learning is avoided.
In a second aspect, an embodiment of the present application provides a system for pushing a job, including: the acquisition unit is used for acquiring historical learning data and judging whether the historical learning data and time meet a first preset condition or not; the determining unit is used for determining the range of the knowledge points involved in the operation according to the historical learning data if a first preset condition is met; the pushing unit is used for determining the knowledge points needing to be learned through operation according to the knowledge point range and pushing the operation matched with the knowledge points; and the judging unit is used for acquiring the homework answer data of the students, judging whether the homework answer data meets a second preset condition or not, and stopping homework pushing when the second preset condition is met.
Further, the acquisition unit includes: the acquisition subunit is used for acquiring historical learning data of students; and the trigger subunit is used for starting the operation pushing system when a first preset condition is met.
Further, the determining unit includes: the analysis subunit is used for analyzing the current mastering probability of the knowledge points based on the depth knowledge tracking model; and the predicting subunit is used for predicting the probability of the student mastering each knowledge point through the homework based on the multi-level mixed model.
Further, the pushing unit includes: the knowledge graph subunit is used for storing and updating the knowledge graph; a job storage subunit for storing a job; the determining subunit is used for determining the next knowledge point needing to be learned through the operation; and the job pushing subunit is used for pushing the job matched with the knowledge point.
Further, the judging unit includes: the updating subunit is used for updating the knowledge point mastery degree of the student; and the judging subunit is used for judging whether the student grasps the learned knowledge points.
In a third aspect, an apparatus is provided in an embodiment of the present application, and includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for job pushing according to any one of the first aspect when executing the computer program.
In a fourth aspect, the present application provides a storage medium for storing instructions that, when executed on a computer, cause the computer to perform the method for job pushing according to any one of the first aspect.
In a fifth aspect, an embodiment of the present application provides a computer program product, which when running on a computer, causes the computer to execute the method for pushing a job according to any one of the first aspect.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part may be learned by the practice of the above-described techniques of the disclosure, or may be learned by practice of the disclosure.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a system structure diagram of a server to which a method for pushing a job provided by an embodiment of the present application is applied;
fig. 2 is a schematic flowchart of a job pushing method provided in an embodiment of the present application;
fig. 3 is a schematic flowchart of a job pushing method provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a job pushing system according to an embodiment of the present application;
fig. 5 is a block diagram of a device structure for pushing a job according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
The method for homework pushing provided by the embodiment of the application can be applied to the field of homework pushing, historical learning data of students are obtained, the knowledge mastering degree of the students is obtained, further, the knowledge points needing to be learned by the students are determined according to the knowledge mastering degree, and proper homework is recommended to the students for practice, and the homework answering data of the students are extracted in the student answering process so as to update the mastering probability of the students to the learned knowledge points in real time to judge whether the students master the knowledge points, so that whether the students need to practice the knowledge points can be judged in time, the energy of the students can be prevented from being wasted, and the learning efficiency of the students is improved. Illustratively, the method for pushing the job of the embodiment of the application can be applied to a server. Referring to fig. 1, fig. 1 is a system structure diagram of a server to which a method for pushing a job provided by an embodiment of the present application is applied, where the server 10 may be a computer or a device that provides and processes network resources on a network and responds to a service request of the terminal, and the teacher terminal 20 and the student terminals 30 may be various devices for sending and receiving information, including (but not limited to): computer, tablet, cell phone, smart tv, terminal may be any data device that communicates with a server via a wireless channel and/or via a wired channel. Different terminals may be incorporated in the same system. The terminals may be mobile or stationary. Terminals may be referred to by a variety of names, such as user equipment, user devices, mobile devices, remote devices, handheld devices, and the like.
Exemplarily, please refer to fig. 2, where fig. 2 is a schematic flowchart of a method for pushing a job provided in an embodiment of the present application, and the method includes:
step S110, acquiring historical learning data, and judging whether the historical learning data meets a first preset condition.
Illustratively, when applied to the server 10, the server 10 may receive the historical learning data transmitted by the student terminals 30, and the server 10 determines whether the received historical learning data satisfies the first preset condition.
Optionally, in step S110, a method for pushing a job provided in an embodiment of the present application includes: acquiring historical learning data, and judging whether the historical learning data meets a trigger condition.
For example, the server 10 determines whether the received history learning data satisfies a preset trigger condition.
Optionally, the trigger condition includes: weak knowledge points are displayed on classroom learning data of students in first preset time, forgetting knowledge points are displayed on classroom learning data of the students in the first preset time, knowledge points to be strengthened on the classroom learning data of the students in the first preset time are displayed on the classroom learning data of the students in the first preset time, and when one or more of the weak knowledge points and the forgetting knowledge points are met, the server 10 judges that historical learning data meet a trigger condition.
Illustratively, the server 10 receives historical learning data of students, determines that weak knowledge points exist in classroom learning data of the students in a first preset time or knowledge points to be strengthened exist, and the server 10 determines that the students need to learn the knowledge points again.
Optionally, the historical learning data comprises: the system comprises evaluation process data, evaluation result data, exercise data, teaching video watching data and character analysis and viewing data.
Illustratively, the evaluation process data may include: the type and the number of questions to be made, and the rate of making questions; the evaluation result data may include: right and wrong answers, evaluation scores and a weak knowledge point list; the exercise data may include: making question sequences and knowledge points related to the questions; the instructional video viewing data may include: learning range and watching duration related to the video; the text parsing view data may include: the knowledge points related to the character analysis, the correctness and the fraction of the question corresponding to the character analysis, and the duration of the character analysis are checked; the server 10 can clearly judge the proficiency level of the student on the application of the related knowledge points according to the evaluation process data, the evaluation result data and the like in the historical learning data, so as to judge whether the knowledge points are weak knowledge points or knowledge points to be improved of the student.
Optionally, the first preset time may be one week, and the classroom learning data of the student in the one week period may show the recent mastering condition of the content learned by the student, so that the problems of the student in learning can be reflected relatively in time to check for omission.
Illustratively, the weak knowledge points may be knowledge points which are sparsely exercised by the students, and the knowledge points to be improved may be knowledge points which can be exercised by the students but are not sufficiently skilled.
Optionally, in step S110, the method for pushing the job provided by the embodiment of the present application further includes: and judging whether the current time point belongs to the preset trigger time.
For example, the server 10 determines whether the current time point is a preset trigger time.
Optionally, the preset trigger time includes: the method comprises the steps that when a teacher quits an online classroom, the teacher triggers manually, and the teacher triggers automatically at regular time each week, the server 10 judges that the current time point meets a first preset condition when the current time point is a certain preset trigger time.
Illustratively, the preset trigger time may be a time that the teacher manually triggers, the teacher manually inputs a trigger instruction to push a job to the student terminals 30 of all students at the teacher terminal 20, or when the teacher exits the online classroom after the online classroom teaching lesson ends, the teacher terminal 20 automatically sends a trigger instruction to push a job to the student terminals 30 of all students to the server 10, or the teacher presets an automatic trigger time at 17 pm of friday by the teacher terminal 20, when it is 17 pm of friday, the teacher terminal 20 automatically sends a trigger instruction to push a job to the student terminals 30 of all students to the server 10, and after the server 10 receives the trigger instruction sent by the teacher terminal 20, further, if the server 10 judges that a weak knowledge point or a to-be-enriched knowledge point exists in the learning data in the first preset time sent by one of the student terminals 30, the server 10 judges that the first preset condition is satisfied, thereby starting a task of job pushing; if the server 10 judges that the classroom learning data in the first preset time sent by a certain student terminal 30 has no weak knowledge points, and the knowledge points to be strengthened and the knowledge points to be forgotten do not carry out the pushing task.
And step S120, if a first preset condition is met, determining a knowledge point range related to the operation according to the historical learning data.
Illustratively, after judging that the first preset condition is met, the server 10 determines the range of knowledge points involved by the job needing to be pushed according to the received historical learning data.
Optionally, in step S120, a method for pushing a job provided in an embodiment of the present application includes: determining the learning range of the student within a second preset time according to the historical learning data; analyzing the mastering probability of the knowledge points in the learning range based on a depth knowledge tracking model; according to the grasping probability, predicting the probability of the student grasping each knowledge point through the homework through a multi-level mixed model; and determining the range of the knowledge points involved in the operation according to the predicted knowledge point mastering probability.
Illustratively, the server 10 determines a learning range of the student within a second preset time according to the historical learning data, then analyzes knowledge point grasping probabilities of the student within the learning range based on the deep knowledge tracking model, thereby predicting the probability of the student grasping each knowledge point through the homework through the multi-level hybrid model, and further, the server 10 determines a knowledge point range involved by the homework needing to be pushed according to the predicted knowledge point grasping probabilities.
Optionally, the second preset time may be two months, the server 10 may check the knowledge points mastered within the learning range of the student in the past two months, and classify the knowledge points that are not mastered by the student in time within the past several first preset time periods into the knowledge points learned by the student, and meanwhile, the increase of the number of the knowledge points may make the hierarchical relationship between the knowledge points that are not mastered more clear, so as to better plan the order of learning the knowledge points by the student.
For example, the server 10 may use the knowledge points with the knowledge point grasping probability lower than ninety percent as the knowledge points that the student needs to learn through the homework and push the matched homework to the student terminal 30, and further, among the knowledge points with the knowledge point grasping probability lower than ninety percent, the knowledge points with the predicted higher knowledge point grasping probability may be used as the knowledge points related to the homework that the server 10 preferentially pushes, so that the knowledge points that the student can learn more easily can be preferentially learned, and the enthusiasm of learning for the student is improved.
And step 130, determining the knowledge points needing to be learned through operation according to the knowledge point range, and pushing the operation matched with the knowledge points.
Illustratively, the server 10 determines knowledge points that the student needs to learn through the homework within the knowledge point range based on the knowledge point range to push the homework matching the determined knowledge points to the student terminal 30.
Optionally, in step S130, a method for pushing a job provided in an embodiment of the present application includes: determining knowledge points needing to be learned in operation in the knowledge point range based on a knowledge graph according to the historical learning data; and pushing the operation matched with the knowledge point.
Illustratively, the server 10 determines a next knowledge point in the knowledge point range, which the student needs to learn in the homework, by using the knowledge map based on the history learning data, and further, pushes the homework matching the knowledge point to the student terminal 30.
Optionally, the knowledge-graph comprises: the server 10 can judge the priority order of knowledge point learning according to the inter-knowledge point hierarchy relationship through the knowledge point list and the inter-knowledge point hierarchy relationship in the knowledge map so as to push proper learning content to the student terminal 30.
And step 140, acquiring the homework answer data of the students, judging whether the homework answer data meets a second preset condition, and terminating homework pushing when the homework answer data meets the second preset condition.
Illustratively, the server 10 extracts the homework answer data in the student answering process to determine whether the homework answer data satisfies the second preset condition, and when satisfied, the server 10 terminates the pushing of the homework to the student terminal 30.
Optionally, in step S140, a method for pushing a job provided in an embodiment of the present application includes: extracting homework answering data of students; judging whether the number of the knowledge points mastered by the student reaches a first preset upper limit or not according to the homework answer data, and judging that the second preset condition is met when the number of the knowledge points mastered by the student reaches the first preset upper limit; or judging whether the number of the operation questions reaches a second preset upper limit or not according to the operation answer data, and judging that the second preset condition is met when the number of the operation questions reaches the second preset upper limit.
Illustratively, the server 10 extracts the homework answer data in the student answering process sent by the student terminal 30, and according to the homework answer data, the server 10 determines whether the number of knowledge points mastered by the student reaches a first preset upper limit, or according to the homework answer data, the server 10 determines whether the number of homework subjects made by the student reaches a second preset upper limit, and when the number of knowledge points mastered by the student reaches the first preset upper limit or the number of the subjects made reaches the second preset upper limit, the server 10 determines that a second preset condition is satisfied, further, the server 10 terminates the pushing of the homework to the student terminal.
Illustratively, the job topic data includes: the correct and wrong, the sequence and the time of the homework answers, and the server 10 can clearly judge the mastering condition of students on the related knowledge points through the correct and wrong and the time of the homework answers.
Illustratively, after receiving the homework pushed by the server 10, the student terminal 30 of the student can answer the question in writing, and then input the combed question solving process into the student terminal 30, the student terminal 30 sends the question solving content of the student to the server 10, after receiving the question solving content of the student, the server 10 takes the time period from the homework pushing to the receiving of the question solving content as the question answering time of the student, and judges whether the question solving content of the student is correct, so as to judge the mastering condition of the student on the learned knowledge point.
Optionally, in determining whether the number of knowledge points mastered by the student reaches a first preset upper limit according to the homework answer data, the method for pushing the homework provided in the embodiment of the present application includes: updating the knowledge point mastering degree of the student according to the homework answering data; and judging whether the student grasps the knowledge points according to the grasping degree of the knowledge points.
Illustratively, the server 10 updates the degree of mastery of the learned knowledge point by the student in real time based on the received assignment data from the student terminal 30, and further determines whether the student has mastered the knowledge point.
For example, the server 10 may specifically express the knowledge point grasping degree as the knowledge point grasping probability, and further, the server 10 may judge that a knowledge point having a knowledge point grasping probability higher than nineteen is grasped, and a knowledge point having a knowledge point grasping probability lower than nineteen continues to push a job matching the knowledge point to the student terminal 30.
In one possible embodiment, when the student still does not grasp the learned knowledge point within the third preset time, the server 10 stops pushing the knowledge point to the student terminal 30, and pushes a more basic knowledge point associated with the knowledge point to the student terminal 30 through the knowledge map, thereby improving the learning efficiency without affecting the learning enthusiasm of the student.
Illustratively, the third preset time may be 3 days, and when the student does not grasp a certain knowledge point through the homework within 3 days, the server 10 pushes a more basic knowledge point.
In a possible implementation manner, after a student has mastered a knowledge point through the homework, the server 10 may delete or continue to retain the knowledge point from the knowledge point range according to whether the knowledge point in the knowledge map has a priority relationship closer to an unsophisticated knowledge point and the importance of the knowledge point in the teaching assessment plan, and when the unsophisticated knowledge point has no priority relationship closer to the knowledge point and the importance of the knowledge point in the teaching assessment plan is lower, the server 10 deletes the knowledge point from the knowledge point range, so that the data processed by the server 10 is reduced, the working efficiency of the server 10 is improved, the processing of the data is more accurate, and the learning of the student can be better planned.
In a possible embodiment, the server 10 determines that the job answer data does not satisfy the second preset condition, and does not terminate the push job. Referring to fig. 3, fig. 3 is a schematic flowchart of a method for pushing a job provided in an embodiment of the present application, and further, in a possible embodiment, the method for pushing a job further includes:
and 150, acquiring the student homework answer data, judging whether the student homework answer data meets a second preset condition, and continuing to push homework when the student homework answer data does not meet the second preset condition.
Exemplarily, in step S150, a method for pushing a job provided by an embodiment of the present application includes: extracting homework answering data of students; updating the mastery degree of the knowledge points of the student pair according to the homework answering data; judging that the students master the knowledge points according to the homework answering data; and judging that the number of the knowledge points mastered by the students does not reach a first preset upper limit, and the number of the homework questions does not reach a second preset upper limit, determining the next knowledge point needing to be learned through homework based on the knowledge map, and pushing the homework matched with the knowledge points.
Illustratively, the server 10 extracts the homework answer data of the student, updates the mastery degree of the student on the learned knowledge point, and judges that the student has mastered the knowledge point, and further, the server 10 judges that the number of the knowledge points mastered by the student does not reach a first preset upper limit, and the number of the made questions does not reach a second preset upper limit, determines the next knowledge point to be learned through the homework based on the knowledge map, and pushes the homework matched with the knowledge point to the student terminal 30.
For example, the first preset upper limit of the number of knowledge points determined by the server 10 may be 15, the second preset upper limit of the number of homework topics may be 45, when the server 10 determines that the student has mastered a certain knowledge point, the server 10 further determines that the number of knowledge points mastered by the student is 12, the number of topics made is 39, the number of knowledge points mastered by the student does not reach the first preset upper limit, and the number of topics made reaches the second preset upper limit, then the next knowledge point is determined based on the knowledge map, and the homework matched with the knowledge point is pushed to the student terminal 30.
Optionally, after step S140, the method for pushing the job provided by the embodiment of the present application further includes: a job data report is generated.
Illustratively, after the server 10 terminates the pushing of the assignment to the student terminals, assignment data reports are generated and sent to the teacher terminal 20 and the student terminals 30 for the teacher and the students to view, so that the students can learn about their specific learning situation.
The job data report may include a question making sequence, a question answering error, a correct rate, a question making time, a grasped knowledge point list, a weak knowledge point list, a knowledge point grasping rate, and the like.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a job pushing system according to an embodiment of the present application. It should be understood that the system in fig. 4 corresponds to the method embodiments in fig. 2 to 3, and can perform the steps related to the method embodiments, and the specific functions of the system can be referred to the description above, and the detailed description is appropriately omitted here to avoid redundancy. The system includes at least one software functional module that can be stored in memory in the form of software or firmware or solidified in the operating system of the system. Specifically, the system comprises:
an obtaining unit 410, configured to obtain historical learning data, and determine whether the historical learning data meets a first preset condition;
a determining unit 420, configured to determine, according to the historical learning data, a knowledge point range related to a job if a first preset condition is met;
the pushing unit 430 is configured to determine a knowledge point required to be learned through a job according to the knowledge point range, and push a job matched with the knowledge point;
the judging unit 440 is configured to obtain the assignment answer data of the student, judge whether the assignment answer data meets a second preset condition, and terminate assignment pushing when the second preset condition is met.
In a possible embodiment, the obtaining unit 410 comprises:
the acquisition subunit is used for acquiring historical learning data of students;
and the trigger subunit is used for starting the operation pushing system when a first preset condition is met.
In one possible embodiment, the determining unit 420 includes:
the analysis subunit is used for analyzing the current mastering probability of the knowledge points based on the depth knowledge tracking model;
and the predicting subunit is used for predicting the probability of the student mastering each knowledge point through the homework based on the multi-level mixed model.
In a possible embodiment, the pushing unit 430 comprises:
the knowledge graph subunit is used for storing and updating the knowledge graph;
a job storage subunit for storing a job;
the determining subunit is used for determining the next knowledge point needing to be learned through the operation;
and the job pushing subunit is used for pushing the job matched with the knowledge point.
In one possible embodiment, the determining unit 440 includes:
the updating subunit is used for updating the knowledge point mastery degree of the student;
and the judging subunit is used for judging whether the student grasps the learned knowledge points.
In a possible embodiment, a system for pushing a job provided by an embodiment of the present application further includes: a generating unit 450 for generating a job data report.
Fig. 5 shows a structural block diagram of a job pushing apparatus according to an embodiment of the present application, where fig. 5 is a block diagram of an apparatus for job pushing. The device may include a processor 510, a communication interface 520, a memory 530, and at least one communication bus 540. Wherein a communication bus 540 is used to enable the connection communication between these components. The communication interface 520 of the device in the embodiment of the present application is used for performing signaling or data communication with other node devices. Processor 510 may be an integrated circuit chip having signal processing capabilities.
The processor 510 may be a general-purpose processor, including a central processing unit, a network processor, etc.; but may also be a digital signal processor, an application specific integrated circuit, an off-the-shelf programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application can be implemented or performed. A general purpose processor may be a microprocessor or the processor 510 may be any conventional processor or the like.
The memory 530 may be a random access memory, a read only memory, a programmable read only memory, an erasable read only memory, an electrically erasable read only memory, or the like. The memory 530 stores computer readable instructions that, when executed by the processor 510, cause the device to perform the various steps involved in the method embodiment of fig. 2 described above.
The memory 530, the processor 510, and the peripheral interface elements are electrically connected to each other, directly or indirectly, to enable data transmission or interaction. For example, these elements may be electrically coupled to each other via one or more communication buses 540. The processor 510 is adapted to execute executable modules stored in the memory 530, such as software functional modules or computer programs comprised by the device.
It will be appreciated that the configuration shown in figure 5 is merely illustrative and that the apparatus may also include more or fewer structural components than shown in figure 5 or have a different configuration than shown in figure 5. The components shown in fig. 5 may be implemented in hardware, software, or a combination thereof.
An embodiment of the present application further provides a storage medium, where instructions are stored in the storage medium, and when the instructions are run on a computer, when the computer program is executed by a processor, the method in the method embodiment can be implemented, and in order to avoid repetition, details are not repeated here.
The present application also provides a computer program product which, when run on a computer, enables the computer to perform the method described in the method embodiments above.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The above-described apparatus embodiments are merely illustrative. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. Further, it is also noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: u disk, removable hard disk, read only memory, random access memory, magnetic or optical disk, etc. for storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method of job pushing, comprising:
acquiring historical learning data, and judging whether the historical learning data meets a first preset condition;
if the first preset condition is met, determining the range of the knowledge points involved in the operation according to the historical learning data;
determining knowledge points needing to be learned through operation according to the knowledge point range, and pushing operation matched with the knowledge points;
and acquiring homework answer data of students, judging whether the homework answer data meets a second preset condition, and stopping homework pushing when the homework answer data meets the second preset condition.
2. The method for pushing a job according to claim 1, wherein the determining whether the historical learning data satisfies a first preset condition comprises:
judging whether the historical learning data meets a trigger condition;
the trigger conditions include: weak knowledge points are displayed on classroom learning data of students in first preset time, forgetting knowledge points are displayed on classroom learning data of the students in the first preset time, and one or more kinds of knowledge points to be strengthened of the students are displayed on the classroom learning data of the students in the first preset time.
3. The method for pushing a job according to claim 2, wherein the determining whether the historical learning data satisfies a first preset condition further comprises:
judging whether the current time point belongs to preset trigger time or not;
the preset trigger time comprises the following steps: the teacher quits the time of the online classroom, the manual trigger time of the teacher and the automatic trigger time of each week.
4. The method for pushing the job according to claim 1, wherein if a first preset condition is met, determining the range of the knowledge points involved in the job according to the historical learning data comprises:
determining the learning range of the student within a second preset time according to the historical learning data;
analyzing the mastering probability of the knowledge points in the learning range based on a depth knowledge tracking model;
according to the grasping probability, predicting the probability of the student grasping each knowledge point through the homework through a multi-level mixed model;
and determining the range of the knowledge points involved in the operation according to the predicted knowledge point mastering probability.
5. The method for pushing the job according to claim 1, wherein the determining the knowledge points needing to be learned through the job according to the knowledge point range and pushing the job matched with the knowledge points comprises:
determining knowledge points needing to be learned in operation in the knowledge point range based on a knowledge graph according to the historical learning data;
and pushing the operation matched with the knowledge point.
6. The method for pushing homework according to claim 1, wherein said obtaining student homework answer data, and said determining whether said homework answer data satisfies a second predetermined condition comprises:
extracting homework answering data of students;
judging whether the number of the knowledge points mastered by the students reaches a first preset upper limit or not according to the homework answer data, and judging that the second preset condition is met when the number of the knowledge points mastered by the students reaches the first preset upper limit;
or judging whether the number of the operation questions reaches a second preset upper limit or not according to the operation answer data, and judging that the second preset condition is met when the number of the operation questions reaches the second preset upper limit.
7. The method of claim 6, wherein the determining whether the number of knowledge points mastered by the student reaches a first preset upper limit according to the homework answer data comprises:
updating the knowledge point mastering degree of the student according to the homework answering data;
and judging whether the student grasps the knowledge points according to the grasping degree of the knowledge points.
8. A system for job pushing, comprising:
the acquisition unit is used for acquiring historical learning data and judging whether the historical learning data meets a first preset condition;
the determining unit is used for determining the range of the knowledge points involved in the operation according to the historical learning data if a first preset condition is met;
the pushing unit is used for determining the knowledge points needing to be learned through operation according to the knowledge point range and pushing the operation matched with the knowledge points;
and the judging unit is used for acquiring the homework answer data of the students, judging whether the homework answer data meets a second preset condition or not, and stopping homework pushing when the second preset condition is met.
9. An apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method of job pushing according to any one of claims 1 to 7 when executing the computer program.
10. A storage medium storing instructions for causing a computer to perform a method of job pushing according to any one of claims 1 to 7 when the instructions are run on the computer.
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Application publication date: 20200428 |